As this is the very first exercise in this workshop, it is relatively easy and short. Its purpose is to get used to this exercise format and, more importantly, to install all necessary packages for this course.

You can find the solutions for this exercise and all other exercises in the ./solutions folder in the repo/directory that contains the course materials. You can copy code from these exercise files by clicking on the small blue clipboard icon in the code boxes’ upper right corner.

Note: We recommend you to use the ./MY_CODE folder to store your R-script files for this course. If you do that, you have to make sure that your script uses the root directory of the course materials as working directory. You can check the current working directory with getwd() and set it with setwd() accordingly.

A presumably more elegant solution would be to use RStudio and open the tidyverse-workshop-esra-2021.Rproj to open it as a project. In this case, the path is already set.

And here comes our very first exercise:

1

Please install and/or load the tidyverse package.

We always recommend using the easypackages packages, which can be installed with the command install.packages("easypackages"). After loading the package with library(easypackages) you can load and install packages with the command easypackages::packages("fancy_package_1", "fancy_package_2", ...).

solution 

easypackages::packages(
  "tidyverse"
)
## All packages loaded successfully

And here’s another quick exercise.

2

Load R’s built-in dataset USAarrests and print it.

You could either enter USAarrests directly in the console or use the print() function.

solution 

data("USArrests")

USArrests
##                Murder Assault UrbanPop Rape
## Alabama          13.2     236       58 21.2
## Alaska           10.0     263       48 44.5
## Arizona           8.1     294       80 31.0
## Arkansas          8.8     190       50 19.5
## California        9.0     276       91 40.6
## Colorado          7.9     204       78 38.7
## Connecticut       3.3     110       77 11.1
## Delaware          5.9     238       72 15.8
## Florida          15.4     335       80 31.9
## Georgia          17.4     211       60 25.8
## Hawaii            5.3      46       83 20.2
## Idaho             2.6     120       54 14.2
## Illinois         10.4     249       83 24.0
## Indiana           7.2     113       65 21.0
## Iowa              2.2      56       57 11.3
## Kansas            6.0     115       66 18.0
## Kentucky          9.7     109       52 16.3
## Louisiana        15.4     249       66 22.2
## Maine             2.1      83       51  7.8
## Maryland         11.3     300       67 27.8
## Massachusetts     4.4     149       85 16.3
## Michigan         12.1     255       74 35.1
## Minnesota         2.7      72       66 14.9
## Mississippi      16.1     259       44 17.1
## Missouri          9.0     178       70 28.2
## Montana           6.0     109       53 16.4
## Nebraska          4.3     102       62 16.5
## Nevada           12.2     252       81 46.0
## New Hampshire     2.1      57       56  9.5
## New Jersey        7.4     159       89 18.8
## New Mexico       11.4     285       70 32.1
## New York         11.1     254       86 26.1
## North Carolina   13.0     337       45 16.1
## North Dakota      0.8      45       44  7.3
## Ohio              7.3     120       75 21.4
## Oklahoma          6.6     151       68 20.0
## Oregon            4.9     159       67 29.3
## Pennsylvania      6.3     106       72 14.9
## Rhode Island      3.4     174       87  8.3
## South Carolina   14.4     279       48 22.5
## South Dakota      3.8      86       45 12.8
## Tennessee        13.2     188       59 26.9
## Texas            12.7     201       80 25.5
## Utah              3.2     120       80 22.9
## Vermont           2.2      48       32 11.2
## Virginia          8.5     156       63 20.7
## Washington        4.0     145       73 26.2
## West Virginia     5.7      81       39  9.3
## Wisconsin         2.6      53       66 10.8
## Wyoming           6.8     161       60 15.6

3

Now, convert the data to a tibble and print it. Compare it to the previous output.

You need the tibble::as_tibble() function.

solution 

USArrests_tibble <-
  tibble::as_tibble(USArrests)

USArrests_tibble
## # A tibble: 50 x 4
##    Murder Assault UrbanPop  Rape
##     <dbl>   <int>    <int> <dbl>
##  1   13.2     236       58  21.2
##  2   10       263       48  44.5
##  3    8.1     294       80  31  
##  4    8.8     190       50  19.5
##  5    9       276       91  40.6
##  6    7.9     204       78  38.7
##  7    3.3     110       77  11.1
##  8    5.9     238       72  15.8
##  9   15.4     335       80  31.9
## 10   17.4     211       60  25.8
## # ... with 40 more rows

4

Feed the data into the function dplyr::glimpse(). What do you think happens to the output?

Compare it to the previous outputs.

solution 

dplyr::glimpse(USArrests_tibble)
## Rows: 50
## Columns: 4
## $ Murder   <dbl> 13.2, 10.0, 8.1, 8.8, 9.0, 7.9, 3.3, 5.9, 15.4, 17.4, 5.3, 2.6, 10.4, 7.2, 2.2, 6.0, 9.7, 15.4, 2.1, 11.3, 4.4, 12.1, 2.7, 16.1~
## $ Assault  <int> 236, 263, 294, 190, 276, 204, 110, 238, 335, 211, 46, 120, 249, 113, 56, 115, 109, 249, 83, 300, 149, 255, 72, 259, 178, 109, 1~
## $ UrbanPop <int> 58, 48, 80, 50, 91, 78, 77, 72, 80, 60, 83, 54, 83, 65, 57, 66, 52, 66, 51, 67, 85, 74, 66, 44, 70, 53, 62, 81, 56, 89, 70, 86,~
## $ Rape     <dbl> 21.2, 44.5, 31.0, 19.5, 40.6, 38.7, 11.1, 15.8, 31.9, 25.8, 20.2, 14.2, 24.0, 21.0, 11.3, 18.0, 16.3, 22.2, 7.8, 27.8, 16.3, 35~
# dplyr::glimpse() provides another method of displaying the data. In such small 
# datasets, in doesn't make a hughe difference. But as tibbles' output are 
# reduced for large datasets, it provides a convenient method of getting a quick 
# glimpse (haha) on the data.

5

Finally, reload the data. Convert it to a tibble and apply dplyr::glimpse() in one %>%-workflow.

Remember that the data object precedes the function call: x %>% f(.)

solution 

data("USArrests")

USArrests %>% 
  tibble::as_tibble() %>% 
  dplyr::glimpse()
## Rows: 50
## Columns: 4
## $ Murder   <dbl> 13.2, 10.0, 8.1, 8.8, 9.0, 7.9, 3.3, 5.9, 15.4, 17.4, 5.3, 2.6, 10.4, 7.2, 2.2, 6.0, 9.7, 15.4, 2.1, 11.3, 4.4, 12.1, 2.7, 16.1~
## $ Assault  <int> 236, 263, 294, 190, 276, 204, 110, 238, 335, 211, 46, 120, 249, 113, 56, 115, 109, 249, 83, 300, 149, 255, 72, 259, 178, 109, 1~
## $ UrbanPop <int> 58, 48, 80, 50, 91, 78, 77, 72, 80, 60, 83, 54, 83, 65, 57, 66, 52, 66, 51, 67, 85, 74, 66, 44, 70, 53, 62, 81, 56, 89, 70, 86,~
## $ Rape     <dbl> 21.2, 44.5, 31.0, 19.5, 40.6, 38.7, 11.1, 15.8, 31.9, 25.8, 20.2, 14.2, 24.0, 21.0, 11.3, 18.0, 16.3, 22.2, 7.8, 27.8, 16.3, 35~